Although a technology is conventionally used to identify a person from other persons by utilizing physical features of an individual in a security system of entry management, access to computer, etc., in a method that takes a picture of a face, a palm print, etc., with a CCD (Charge Coupled Device) camera, etc., to make an input image and that verifies the input image and registered images of faces, palm prints, etc., registered in advance to identify a person, high identification capability cannot be realized just by overlaying the input image and a plurality of registered images to compare degree of correspondence since facial expressions, how much the palm is opened, shooting angles, etc., are different.
An Eigenface method is generally known as an image recognition method that checks whether there is a match between an input image and registered images to identify a person. The Eigenface method normalizes sizes of the images in an image group to generate subspaces of feature vectors consisting of gray values of pixels of the normalized images by principal component analysis and projects the feature vectors of the input image and the registered images over to the subspaces and calculates a degree of similarity to conduct verification and determination.
The conventional technology has a problem of deteriorating the capability of distinguishing a person from other persons when distortion, deviation, etc., of an input face-image occurs, from the registered face images, due to differences in facial expressions, shooting angles, etc., because the verification of the input face-image and the registered face images is conducted based on fixed pixels between the input image and the registered images.
Another technology is disclosed (see, e.g., non-patent document 1) that sets sample points to parts that are features of faces such as eyes, noses, mouths, etc., of registered images of faces. The technology finds positions of sample points on the input image corresponding to the sample points on the registered images by calculating the degree of similarity of both images and compares local features of both images at the sample points found.
This conventional technology alleviates the problem and can enhance the capability of distinguishing a person from other persons since the technology can detect sample points on an input image corresponding to the sample points on the registered images by calculating the degree of similarity even when distortion or deviation between registered images and the input image occurs due to differences in facial expressions, shooting angles, etc.
Non-patent document 1: L. Wiskott and three others, “Face Recognition by Elastic Bunch Graph Matching”, IEEE Transactions On Pattern Analysis And Machine Intelligence, vol. 19, no. 7, pp. 775-779, 1997.